Though prior attempts have been made in data augmentation for malware detection, they do not yet perform meaningful data augmentation. In [1]}, [2]}, data augmentation for malware detection is done by representing programs as sequences of opcodes and replacing one opcode with another without preserving semantics. Further, [3]} augments images generated from malware, which are known to be flawed representation [4]}. In contrast to these efforts, Marvolo's contributions lie in (1) a deep-dive analysis of large-scale malware datasets to uncover the usage patterns of semantics-preserving code transformations by malware authors, and (2) a system that leverages those insights to efficiently grow small datasets into larger ones with improved heterogeneity and realism that aid end-to-end ML-based malware detection.
